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Found 18 papers, 5 papers with code
Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules
Automated game design (AGD), the study of automatically generating game rules, has a long history in technical games research.
Active Learning for Classifying 2D Grid-Based Level Completability
Determining the completability of levels generated by procedural generators such as machine learning models can be challenging, as it can involve the use of solver agents that often require a significant amount of time to analyze and solve levels.
Game Level Blending using a Learned Level Representation
Game level blending via machine learning, the process of combining features of game levels to create unique and novel game levels using Procedural Content Generation via Machine Learning (PCGML) techniques, has gained increasing popularity in recent years.
Level Assembly as a Markov Decision Process
Many games feature a progression of levels that doesn't adapt to the player.
tile2tile: Learning Game Filters for Platformer Style Transfer
We present tile2tile, an approach for style transfer between levels of tile-based platformer games.
Latent Combinational Game Design
This enables generating new games that blend the input games as well as controlling the relative proportions of each game in the blend.
Gram-Elites: N-Gram Based Quality-Diversity Search
By integrating structure into operators, instead of fitness, these genetic operators could be beneficial to QD in PCGML.
A Reasoning Engine for the Gamification of Loop-Invariant Discovery
Within an hour, players are able to specify and verify properties of programs that are beyond the capabilities of fully-automated tools.
Procedural Content Generation using Behavior Trees (PCGBT)
Behavior trees (BTs) are a popular method for modeling NPC and enemy AI behavior and have been widely used in commercial games.
Dungeon and Platformer Level Blending and Generation using Conditional VAEs
Variational autoencoders (VAEs) have been used in prior works for generating and blending levels from different games.
Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder
We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games.
Conditional Level Generation and Game Blending
Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data.
Towards Game Design via Creative Machine Learning (GDCML)
In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks.
Sequential Segment-based Level Generation and Blending using Variational Autoencoders
In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments.
Controllable Level Blending between Games using Variational Autoencoders
We then use this space to generate level segments that combine properties of levels from both games.
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero.
Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games
Deck building is a crucial component in playing Collectible Card Games (CCGs).
